With this motivation, we first parse caption into the noun \(t_{i}^{1}\)  and verb \(t_{i}^{2}\)  levels, followed by linear layers. We utilize linear layers to embed corresponding video features \(v_{i}^{1}\) , \(v_{i}^{2}\)  and use a self-attention layer to exploit contextualized features.
Then, we concatenate textual and visual features to compute the distance between these representations, \(\hat{v_{i}}\)  and \(\hat{t_{i}}\) . L1 and L2 refer to triplet losses. The more details of the loss functions are in Eq. REF  and baseline paper [1]} and the architecture details are in Fig.  REF
